Automatically extracts features from time series data for improved forecasting.
problem Manual feature selection for time series forecasting is inefficient and prone to errors.
method Extracts features from time series using recurrence plots and computer vision algorithms.
result Automatically extracted features lead to highly comparable and sometimes superior forecasting performance.
Dilated CNN improves multivariate time series classification.
problem Multivariate time series classification.
method Transformed multivariate time series into image-like style, applied dilated and strided convolutions.
result Automatic features extracted by dilated CNN are as effective as hand-crafted features.
Paper proposes NNAFC for automatic financial factor construction.
problem Manual factor construction is time-consuming and prone to bias.
method NNAFC uses neural networks to automatically construct diversified financial factors.
result NNAFC outperforms GP in constructing more informative and diversified factors.
New method extracts radio signal features for automatic modulation classification.
problem Challenges in automatic modulation classification without expert-defined features.
method Biologically-inspired regularized stacked sparse denoising autoencoders (SSDAs).
result Correct classification rates > 99% at 7.5 dB SNR and > 92% at 0 dB SNR.
End-to-end model extracts nested terms without extra features.
problem Automatic term extraction for nested terms.
method Deep learning model that predicts conceptual terms within fixed sentence lengths.
result High recall and comparable precision on term extraction task.
Paper proposes CNN for automatic sales forecast in e-commerce.
problem Challenges in sales forecasting in e-commerce.
method Uses Convolutional Neural Network to automatically extract features from raw log data.
result Experimental validation shows the effectiveness of the proposed method.
Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarit…
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
New framework discovers roles of edges in graphs.
problem Previous work focused on node roles, this tackles edge roles.
method Generalizable framework for learning and extracting edge roles from arbitrary graphs.
result Demonstrates utility of edge roles for network analysis.
Study improves app feature extraction models with new annotation guidelines and data.
problem Improving the quality and usefulness of app feature extraction models.
method Exploring the effects of annotation guidelines and annotated data on app feature extraction models.
result New annotation guidelines lead to less noisy and more informative app features.
New method extracts patterns from program logs and embeds them for detection.
problem Real-world malicious software detection.
method Extract patterns from behavior graph, embed into continuous space using autoencoder.
result Embedding captures interpretable structures in pattern parts.
DeepSleepNet uses CNN and LSTM to score sleep stages from raw EEG data.
problem Automatic sleep stage scoring using raw EEG data.
method Deep learning model using CNN for time-invariant features and LSTM for transition rules.
result DeepSleepNet achieves similar accuracy to state-of-the-art methods on different EEG datasets.
A new method extracts events and their arguments efficiently from text.
problem Efficiently extract event information from texts with long-range dependencies and associations.
method Graph Convolutional Networks with shortest dependency paths to capture syntactic relationships.
result Significant improvement over state-of-the-art methods.
The paper tackles class imbalance in deep learning models and proposes a method to enhance feature extraction.
problem Class imbalance affects deep learning models, especially in imbalanced settings.
method The paper introduces an extension of deep over-sampling to use automatically-generated abstract-labels for weak-supervision.
result The proposed framework significantly improves image classification benchmarks with imbalanced classes.
FFRK automatically extracts features for spatial interpolation without external variables.
problem Spatial interpolation challenges, especially nonstationarity and lack of explanatory variables.
method Feature-Free Regression Kriging (FFRK) method that extracts geospatial features.
result FFRK outperforms classical methods in predicting heavy metal concentrations.
Extracts important peaks from XRD spectra using Attention mechanism.
problem Identifying significant peaks in XRD patterns for material properties.
method Convolutional neural network with Attention mechanism to analyze deep features.
result Selected lattice constant predicts cathodic material cell voltage.
MUS-ROVER learns compositional rules from music.
problem Discovering interpretable rules in music composition.
method Feature learning via n-gram models to extract statistical patterns.
result MUS-ROVER can recover known rules and identify new patterns.
CRAN extracts music highlights using attention and recurrent layers.
problem Extracting valuable music highlights from signals.
method Convolutional Recurrent Attention Networks (CRAN) with attention mechanism.
result CRAN outperforms three baseline methods in highlighting extraction.
A new method uses PSO to optimize sentence weights for user-oriented document summaries.
problem Handling information overload in documents through efficient summarization.
method Particle Swarm Optimization (PSO) to identify and weight sentence features.
result Improved accuracy in summarization compared to previous methods.
Improved spoken English intelligibility with computer recognition and feature extraction.
problem Improving spoken English pronunciation and intelligibility.
method Automatic speech recognition using PocketSphinx alignment and feature extraction with SVM classifier probability prediction.
result SVM models achieve 82 percent agreement with human transcriptions, up from 75 percent.
Develops ACE to automatically identify meaningful concepts from neural network predictions.
problem Challenges in interpreting feature importance scores for machine learning models.
method Proposes concept-based explanation principles and develops ACE algorithm to extract visual concepts.
result Demonstrates ACE discovers human-meaningful, coherent concepts for neural network predictions.
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
This paper improves ASR robustness by learning domain invariant features.
problem Robustness issues in ASR due to mismatched training and testing distributions.
method Factorized Hierarchical Variational Autoencoder (FHVAE) for unsupervised learning of domain invariant features.
result 41% and 27% absolute word error rate reductions on mismatched domains.
CNNPred uses CNNs to predict stock market movements across multiple markets.
problem Feature extraction and prediction in financial markets.
method CNN-based framework for multiple markets, using various data sources.
result Significant improvement in prediction performance compared to baseline algorithms.
Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient…
Autoencoders improve feature extraction for aviation ML tasks.
problem Manual feature processing is labor-intensive, inefficient, and prone to loss.
method Unsupervised learning using autoencoders to extract features.
result Autoencoders can automatically extract effective features and reduce data cleaning workload.
Novel CTG analysis splits signals into balanced windows and uses 1DCNN for automatic feature extraction.
problem Inter- and intra-variability in CTG interpretation, low positive predictive value.
method Split CTG time-series into balanced windows, extract features using 1DCNN and MLP ensemble.
result Normalizes class distributions, reduces reliance on manual feature selection.
PerceptionNet uses deep CNN for late sensor fusion in HAR, improving accuracy.
problem Improving human activity recognition using motion sensor fusion.
method Late 2D convolution on multimodal time-series data.
result PerceptionNet surpasses state-of-the-art methods by 3% average accuracy.
New method identifies key features from unlabeled data, improving classification.
problem Identifying important features in unlabeled datasets.
method Adapts kernel PCA to automatically learn a kernel function for data.
result Learned kernel features significantly improve classification performance.
The paper analyzes sports commentary to automatically recognize events and extract insights.
problem Automatically recognizing and categorizing major actions in sports events from commentary.
method Used multiple Natural Language Processing techniques for classification and sentiment analysis.
result Identified insights from analyzing live sport commentaries and classifying major actions.
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scope-limited features whose predictive power is only applicabl…
Robust ASR model removes fast-changing features to resist attacks.
problem Vulnerability of ASR systems to adversarial attacks.
method Removing fast-changing features using slow feature analysis or low-pass filtering.
result Hybrid ASR models are more than four times more robust against targeted attacks.
A new neural network framework ADNN improves financial feature construction.
problem Constructing highly informative financial features.
method Neural network (ADNN) with domain knowledge, pre-training, and data augmentation.
result ADNN produces more diversified and informative features than genetic programming.
FeatureEnVi aids in feature engineering with visual analytics.
problem Insufficient support for feature engineering in visual analytics tools.
method Stepwise selection and semi-automatic extraction approaches.
result Extracts heavily engineered features evaluated by multiple metrics.
Paper improves reproducibility of AD classification using diffusion MRI.
problem Difficulty in comparing and reproducing classification performance of AD studies using diffusion MRI.
method Extended a framework to ADNI data, including preprocessing and feature extraction. Used non-nested validation and compared different components.
result Diffusion MRI features can achieve comparable performance to T1w MRI, with proper feature selection and validation methods.
QuPWM detects epileptic spikes in MEG signals with high accuracy.
problem Manual detection of epileptic spikes in MEG signals is time-consuming and subjective.
method QuPWM combines PWM and SVM for feature extraction and classification.
result Average accuracy of 98% achieved on a balanced dataset of 3104 samples.
Paper compares different spoofing detection methods for speech verification.
problem Detecting audio replay attacks in speech verification systems.
method GMM based methods, high level features extraction with simple classifier, deep learning frameworks.
result Deep learning approaches are efficient in changing acoustic conditions.
ADNN uses prior knowledge to construct financial features.
problem Feature construction in financial trading.
method Tailored neural network structure with domain knowledge.
result ADNN constructs more informative features than genetic programming.
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, an…
Deep learning improves gait biometric recognition accuracy.
problem Low accuracy in existing CSI-based gait identification systems.
method Developed an end-to-end deep CSI learning system using deep neural networks.
result Achieved a top-1 accuracy of 97.12% for a dataset of 30 people.
A deep learning framework learns wavelet packet transforms for efficient feature extraction.
problem Efficiently extracting meaningful time-frequency features from high-frequency signals.
method Learnable wavelet packet transforms using deep learning.
result Improved spectral leakage and enhanced anomaly detection performance.
Paper proposes a reproducible framework for Alzheimer's disease classification using ADNI data.
problem Lack of reproducible comparison between machine learning approaches in Alzheimer's disease classification.
method Automated database conversion, modular Nipype architecture, feature extraction pipelines, and reproducible classification techniques.
result Highest accuracies achieved for various Alzheimer's disease subtypes.
Automatically extracts data from scatter plots.
problem Challenges in extracting numerical data from scatter plots.
method Uses deep learning for component identification and optical character recognition for pixel-to-coordinate mapping.
result Achieves 89% successful data extraction on test set.
Improved EEG event classification using differential energy.
problem Automatic classification of EEG signals from time frequency representations.
method Comparison of feature extraction techniques, including differential energy and derivatives.
result 24% absolute reduction in error rate, improved discrimination between signal events and noise.
Compact-CNN outperforms traditional methods in SSVEP classification.
problem Decoding SSVEPs without domain-specific knowledge.
method Compact convolutional neural network (Compact-CNN) for automatic feature extraction.
result Across subject mean accuracy of 80% (chance 8.3%) using Compact-CNN.
Automatically extracts phenotypes from cancer clinical notes for genetic studies.
problem Lack of structured patient representations in EHRs.
method Clustering of medical terms and sentences in clinical notes.
result 341 significant associations between clinical features and somatic mutations.
Fed-FEARE model extracts rules from multiple agencies' data securely.
problem Data privacy and secure rule extraction across multiple agencies.
method Federated F-score based ensemble tree model.
result Model performance measures significantly improved with federated learning.
SynergicLearning combines NN and HD models for high accuracy and efficiency.
problem Combining neural networks and hyperdimensional learning for improved accuracy and efficiency.
method Hybrid model combining NN feature extraction and HD classification, parameterized hardware implementation.
result Improves accuracy by at least 10% compared to HD learning models and 1.60x power efficiency.